Key Skills to Become a Data Analyst and Why Is It Rewarding

We are living in the age of data explosions. There are affluent of unprocessed raw data that we generally do not know what to do or how to make sense of it nor know ways to economize it. However, a data analyst can fill up those shoes and help us gain a competitive edge in the market or industry today. With growing data, the demand for the data analyst grows too. And when empowered with disruptive technologies like Artificial Intelligence, Machine Learning, Internet of Things (IoT), data analysts can help businesses thrive and flourish in the days ahead.

Data Analytics refers to qualitative and quantitative techniques, algorithms, and processes used to enhance productivity and business gain. By this, data is extracted, acknowledged, and bifurcated to identify and analyze behavioral data, techniques, and patterns can be dynamic according to a particular business's need or requirement. This is the reason why data analytics has a wide range of scope across several industries. Brands use data for various purposes that align with their objectives and customer demands, making skilled data analysts as one of the most influential people in the modern industrial world.

Irrespective of the job roles or industry data analysts' work, their duties generally comprises of generating and extraction of numeric data, interpreting the data (mostly structured), observing the trends, patterns in the data, product researching, positioning, sentiment analysis, analyze the market. After those, the final task is to generate and present detailed reports of the findings to the concerned authorities to make informed future decisions. So, one can assume that the job of a data analyst is pretty thrilling and challenging too. Hence, you can consider this as an option for a career also.

Why be a Data Analyst?

According to an independent research report by Analytics Insight, Big Data is expected to grow at a CAGR of 10.9% in 2019-2023 while the global market value will be US$301.5 billion by 2023. Therefore, from the given figures, one can understand the vast opportunities that lie ahead in this sector. And if you assume that data analyst's importance is only limited to the role of a Data Analysis, well you might be misguided here. Having a competent Data analytics training is your visa into becoming a business analyst or into the world of Data Science. And being a data scientist is considered as the sexiest job of the 21st century, even by Harvard Business Review.

Other options include Analytics Architect, Metrics and Analytics Specialist, Marketing analyst, Sales analyst, financial analyst, operations analyst, and more. One can also choose from the three types of data analytics depending on the Big Data environment viz., Prescriptive Analytics, Predictive Analytics, and Descriptive Analytics. Moreover, data analysts are compensated well for their work, and with the job market demand outpacing the entry-level candidates, the pay is going to increase in the future.

Besides, there is a massive deficit on the supply side even when demand for Analytics skills is rising steadily. This deficit occurs mainly in positions of Analytics Consultant and Data Scientists. So, there is plenty of job vacancies in the market that can be filled. Even the tools they use are not limited. Data analysts can use either Google Analytics, Tableau (for data aggregation and analyzing), Jupyter notebooks (testing codes), Github (sharing projects), and many more. The exciting workplace environment never makes their work monotonous nor tedious.

Skilling:

The first step to becoming a data analyst is one must a high level of statistical literacy and a natural flair for mathematics. Afterward, one can train oneself by learning the following skills:

Computer Skills: These include programming and scripting Java, C++, MATLAB, Python, PHP, and many more. Data management and manipulation skills are essential too. This comes through languages such as HIVE, R, Scala, and SQL, or Structured Query Language, SQL takes different forms across a wide range of data platforms, such as Microsoft's T-SQL or MySQL, commonly used online. To be able to spot patterns and forecast trends, one can gain proficiency in Microsoft Power BI, SAS, Oracle Visual Analyzer, and Cognos.

Advanced Microsoft Excel: This a critical skill that differentiates a data analyst from a data scientist. Data analysts should have a good handle on excel and understand advanced modeling and analytics techniques.

Data Visualization: Effective data visualization takes trial and error. A successful data analyst understands what types of graphs to use, how to scale visualizations, and knows which charts to use depending on their audience.

Practical Business and Communication Skills: Analysts must be good communicators to ensure the data provided aligns with business objectives and criteria. There may be a time where an analyst has to discuss and collaborate with executives, clients, other IT specialists, and various employees. They should be a good team-player.

Creative and Analytical Thinking: Data is enormous and rapidly booming. Analysts may require to understand techniques for cleansing, organizing, and structuring data to provide efficient and reliable results. For this, it is crucial to have a creative and analytical mindset to be able to arrive at interesting research questions and conclusions. Further, one must have a keen sense of attention to detail to minimize missing incorrect spotting and data redundancy.

To start a career as a data analyst, one can begin with having a bachelor's degree in subjects focusing primarily on mathematics, statistics, computer science, information management, finance, and economics. Then after getting relevant work experience, one can few courses that will help them stay ahead of the curve.

Since analytics help enhances the business value chain, backed by industrial knowledge and have a knack to mine opportunities before the rivals, numerous companies are looking to hire data analysts. A huge array of organizations like Ayata, IBM, Alteryx, Teradata, TIBCO, Microsoft, Platfora, ITrend, Karmasphere, Oracle, Opera, Datameer, Pentaho, Centrofuge, FICO, Domo, Quid, Saffron, Jaspersoft, GoodData, Bluefin Labs, Tracx, Panaroma Software, and countless more are utilizing Big Data Analytics for their business needs and huge job opportunities are possible with them. So what are you waiting for?